Today I was working on a client project when a seemingly
innocent refactoring made the program 2x faster.
Of course, being happy about the improvement and going on with
my life would have been wrong, as random performance improvements
almost always mean that something foul is at play.
I undid the refactoring step by step, until the only change
remaining was that I replaced a use of the forever
function by implementing forever itself, like this:
myForever :: (Monad m) => m () -> m ()
myForever f = do
f
myForever f
How could using this function make my client's application 2x
faster?
Looking at
forever's code, it is basically:
myForever :: (Applicative f) => f a -> f b
myForever f = f *> myForever f
Doing a couple more tests:
myForever :: (Monad m) => m a -> m a
myForever f = f *> myForever f
is slow, but
myForever :: (Monad m) => m a -> m a
myForever f = f >> myForever f
is fast.
So the issue seems to be that *> is 2x slower
than >>. Not great.
Of course the next question is: What Monad m am I
running on? In my program I'm running StateT SomeStuff IO
().
I was promptly reminded by a colleague that I shouldn't be using
StateT over IO as it makes exception handling and
concurrency very
hard, and that I should use a ReaderT instead, but
since the program dealt neither with exceptions nor concurrency,
that shouldn't be relevant here (more on it later).
Digging further into this performance difference, I found that
it went away when upgrading transformers from
0.5.2.0 to 0.5.3.0. Looking at the
transformers changelog, the point
* Added specialized definitions of several methods for efficiency
sounded relevant. I then found the transformers issue *>
must be defined in instances to prevent space leak with
forever, which reports that
forever, used with StateT s IO or
ReaderT r IO has a space leak
The linked GHC
ticket explains that it was introduced when
base-4.9.0.0 (for GHC 8.0) switched
forever to use Applicative. But here I
sit, 15 months later with GHC 8.2, spending hours debugging this
issue for my client.
This is pretty bad.
How can a performance regression like this sneak into a
core library that everybody uses, triggered by a function
as fundamental as forever? And the problem also
applying for ReaderT makes it even more widely spread.
This reminds me of another
similar bug from 4 years ago, where I found that some
operations in the vector package (also fundamental and
widely used) had become 5x slower without anyone noticing, that
time because of some C-preprocessor-macros accidentally being
undefined due to missing header includes.
As a community we make a big fuss about how Haskell allows you
to write correct code and how refactorings are easy and safe. But
we're not promoting a good image of Haskell here. In other
programming languages I rarely encounter performance issues in
parts that are this fundamental and this widely used. Not having
major performance regressions is also a form of correctness.
Right now, as a community, we are simply bad at not
introducing performance regressions. If we want Haskell to
grow, we must do something about that.
So, who is to blame?
- The GHC developers for pushing the generalisation
from
Monad
to Applicative
in forever
and other functions, a
change that has been desired and requested by pretty much all
Haskell users for years? Looks like they did everything right.
Without such changes, Haskell will stagnate and we'll have to live
with historical ballast, forever.
- The
transformers maintainer, for immediately
applying a fix after being shown the problem, in response to code
that he did not change? Looks like he did everything right.
It looks like we cannot blame either of the two parties actively
changing code.
I think the problem is: Lack of performance
tests.
If our tools do not allow us to reason trivially about
performance (rewrite rules unnoticeably not firing or being
commented out, header files unnoticeably not being included,
non-breaking fundamental changes accidentally introducing space
leaks and huge run-time differences), then we must not
rely on human inspection (people reading diffs) to immediately spot
these issues.
Instead, we must write tests to automatically verify that we're
not regressing the performance of core functionality. Such tests
should:
- ensure that the time spent in commonly used functions stays the
same,
- ensure that the space complexity doesn't accidentally change
(for example,
forever on StateT should
not space-leak),
- be ubiquitous in the core libraries we all use.
A good example of this approach is the nofib test
suite in GHC, that exercises common use cases of the compiler, and,
via Continuous Integration and automatically run tests, notifies
the developers if they made something slower. Unfortunately this
approach is extremely rarely seen in any Haskell libraries
higher-level than GHC, and tools that make it easier (such as
weigh)
are recent developments.
Preaching performance tests when they are difficult to implement
isn't exactly useful. To put my (or rather, FP Complete's) money
where my mouth is, I have just published a new library cpu-instruction-counter,
which can measure the CPU instructions executed by a piece of
Haskell code. I encourage you to use it to guard your code against
accidental performance regressions, and it would also be great if
we started using these approaches directly in the test suites of
base, transformers and so on.
By pushing more into this direction and adding performance tests
to fundamental libraries, we can keep improving these fundamentals,
such as moving functions from Monad to
Applicative, and be sure to get notified early if we
accidentally break things, instead of wasting productive days on
evil surprises one and a half years later.
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